Data science typically refers to the science that incorporates various disciplines including, but not limited to, operations research, mathematics, statistics, computer science, and domain-specific expertise. A data scientist thus is one who practices some or all aspects of data science in attempting to solve complex data problems. Such complex data problems may, for example, come up in big data and cloud computing contexts.
A data science project typically runs through a data analytic lifecycle, which includes creation of hypotheses, collection of data, exploration of the data, and execution of analytic models across that data. Typically, there are multiple stakeholder (actor) types involved with a data science project, e.g.: data scientist, data engineer, database administrator, project sponsor, project manager, business intelligence analyst, and business user.
Despite adequately conditioning data, creating analytic models, and following a data analytic lifecycle, many times gauging the success or failure, i.e., outcome, of any given project is very difficult. Understanding hidden drivers for what makes a project successful is difficult to ascertain, and stakeholders in data science projects may be more focused on developing algorithms and obtaining data, than understanding whether their project is quietly failing.